In view of the fact that the network selection algorithm in the heterogeneous wireless network has few quality of service indicators, and the frequent switching of users is becoming more and more serious, In this paper, a vertical handover method for heterogeneous wireless networks based on subjective and objective weighting combined with improved deep reinforcement learning is proposed. Firstly, a software-defined network architecture supporting heterogeneous wireless networks was proposed; secondly, an attribute weighting algorithm combining subjective and objective weighting was proposed; finally, the network selection problem is solved by using Dueling-DQN. The simulation results show that the proposed algorithm reduces the number of switching times by 11.25%, 13.34%, 18.76% and 13.75% respectively under different user types of networks, and increases the throughput by 16.64%. Therefore, the algorithm proposed in this paper effectively avoids ping-pong switching, reduces the number of switching times and improves the throughput.